import tensorflow as tf
from numpy.random import RandomState

# 训练数据大小
batch_size = 8

# 定义神经网络的参数
w1 = tf.Variable(tf.random_normal([2, 3], stddev=1))
w2 = tf.Variable(tf.random_normal([3, 1], stddev=1))

# 输入
x = tf.placeholder(tf.float32, shape=(None, 2), name="x-input")
y_ = tf.placeholder(tf.float32, shape=(None, 1), name="y-input")

# 定义前向传播
a = tf.matmul(x, w1)
y = tf.matmul(a, w2)

# 定义损失函数和反向传播算法
y = tf.sigmoid(y)
cross_entropy = -tf.reduce_mean(y_ * tf.log(y * tf.clip_by_value(y, 1e-10, 1.0) + (1-y) * tf.clip_by_value((1-y), 1e-10,
                                                                                                           1.0)))
train_step = tf.train.AdadeltaOptimizer(0.001).minimize(cross_entropy)

rdm = RandomState(1)
dataset_size = 128

X = rdm.rand(dataset_size, 2)
# x1+x2 为合格
Y = [[int(x1 + x2 < 1)] for (x1, x2) in X]

with tf.Session() as sess:
    init_op = tf.global_variables_initializer()
    sess.run(init_op)
    print(sess.run(w1))
    print(sess.run(w2))

    STEPS = 5000
    for i in range(STEPS):
        start = (i * batch_size) % dataset_size
        end = min(start + batch_size, dataset_size)

        sess.run(train_step, feed_dict={x: X[start: end], y_: Y[start: end]})
        if i % 1000 == 0:
            total_cross_entropy = sess.run(cross_entropy, feed_dict={x: X, y_: Y})
            print("After %d training step, cross entropy on all data is %g" % (i, total_cross_entropy))
    print(sess.run(w1))
    print(sess.run(w2))



